Our full-service platform integrates the best of machine and human intelligence to scale your data labeling and convert your raw data into high-value, model-ready data, without requiring you to label a single piece of it.

Raising the bar on data labeling

Your machine learning model is as only as good as the quality of the data you feed it, yet it’s not scalable to spend 60-80% of a Data Scientist’s time preparing and labeling data. We’ve witnessed early-stage enterprise AI teams burn through their resources building an in-house solution. That’s why we’ve built a full-service platform designed to remove the training data bottleneck and accelerate machine learning projects. We configure our platform and data annotation tools to convert your raw data into model-ready data in fraction of the time, so that Data Scientists can stay focused on their domain expertise.

Data labeling for text, images, video, and audio

Annotation task design and distribution

Human workforce screening, training, and management

Continuous quality control

Data and infrastructure security management

Build model confidence with quality data

We’ve build industry best practices by augmenting millions of pieces of data for Fortune 500 companies who trust Alegion to achieve up to 99% data accuracy for their supervised machine learning models.

Why enterprise customers work with us

Our customers come to us to scale their training data and machine learning initiatives.They stay because of the quality of our data output and the trust they’ve built with our experienced team of Customer Success Managers.

Flexible crowd composition

At the core of the platform is its ability to deploy data labeling tasks to a qualified team of specialists who enrich your data with human judgments. You have the flexibility to integrate Alegion’s own trained team, supply your own private crowd, or a hybrid crowd, including “bringing-your-own-crowd” of domain experts to meet your project-specific needs and security requirements like geographic isolation and data access controls.

Purpose-built annotation tools

Since training data is not one-size-fits-all, we have developed a proprietary set of labeling and annotation tools to expedite data labeling tasks for computer vision and natural language processing with greater accuracy. The ML-augmented annotation tools enable data specialists to classify, label, and segment images, videos, audio, and text, resulting in faster data processing and cost reduction without compromising on quality.

ML-augmented quality control

Predictive algorithms are used to score judgments per task and dynamically determine if additional quality control parameters like judgment consensus, gold standard data, administrative reviews, or exception handling are needed. Our confidence models continually learn from subsequent quality control stages, enabling the platform to improve accuracy on an ongoing basis without increasing time and cost.